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machine learning toolbox in matlab  (MathWorks Inc)


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    MathWorks Inc machine learning toolbox in matlab
    Machine Learning Toolbox In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2278 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+function+ksdensity/Statistics+and+Machine+Learning+Toolbox/pmc07612017-254-26-30
    Average 96 stars, based on 2278 article reviews
    machine learning toolbox in matlab - by Bioz Stars, 2026-09
    96/100 stars

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    Article Title: On the potential sources of a low-frequency sound percept that only a few can perceive.
    Article Snippet: The statistics and machine learning toolbox was used in MATLAB 2024b (The MathWorks, Inc., Natick, Massachusetts, USA) for statistical analysis.

    Article Title: Quantitative mapping of meniscus morphology using advanced imaging analysis in young patients.
    Article Snippet: Principal component analysis (PCA) using the Statistics and Machine Learning Toolbox in MATLAB R2023a) was performed on the vertex coordinates to identify the primary anatomical axes for each meniscus [24].

    Article Title: Normative Data of Higher-Order Aberrations in Healthy Caucasian Eyes and Their Correlation with Age, Gender, and Spherical Equivalent
    Article Snippet: Packages tidyverse and rstatix were used for R and statistics and machine learning toolbox for MATLAB.

    Article Title: Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome.
    Article Snippet: The resulting (40x40) symmetric distance matrix was used for hierarchical clustering with Ward’s minimum-variance linkage (MATLAB R2026a, Statistics and Machine Learning Toolbox).

    Article Title: Pharmacokinetic modelling during long-term anesthesia: minimizing the gap
    Article Snippet: All these tools were used from the Statistics and Machine Learning Toolbox® in MATLAB®.

    Article Title: Tractography-based correlation of diffusion anisotropy metrics in chronic post-stroke aphasia.
    Article Snippet: Classification procedures were implemented using the Statistics and Machine Learning Toolbox (MATLAB R2023a; MathWorks, Natick, MA), which facilitates supervised model development, cross-validation (CV), and quantitative performance assessment.

    Article Title: Protocol for identifying and comparing neuronal ensembles using different algorithms within a graphical user interface
    Article Snippet: MATLAB Statistics and Machine Learning Toolbox , MathWorks , https://www.mathworks.com/products/statistics.html.

    Article Title: Dynamic modulation of social gaze by sex and familiarity in marmoset dyads
    Article Snippet: We used the maximum likelihood estimation method (mle() function in the Statistics and Machine Learning Toolbox in MATLAB) to fit a mixture of Gamma and Gaussian distributions.



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    Image Search Results


    “Scrambled” response time and articulatory variability (A and B) Mean (A) and standard deviation (B) of response time (RT) distributions for all subjects and separately for sEEG implants only, and SDE only. Each data point represents the mean or standard deviation of RT for a single subject. Box-and-whisker plots indicate median, upper and lower quartiles, and extent of outliers. Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production. Time-zero is word onset as manually labeled by inspecting the spectrogram. (D) Probability distribution function of phoneme offsets color-coded from phonemes in (C). These distributions were generated from a subset of subjects ( n = 40) with sufficiently clear audio for automated phonemic transcription.

    Journal: iScience

    Article Title: Supplementary motor area in speech initiation: A large-scale intracranial EEG evaluation of stereotyped word articulation

    doi: 10.1016/j.isci.2024.111531

    Figure Lengend Snippet: “Scrambled” response time and articulatory variability (A and B) Mean (A) and standard deviation (B) of response time (RT) distributions for all subjects and separately for sEEG implants only, and SDE only. Each data point represents the mean or standard deviation of RT for a single subject. Box-and-whisker plots indicate median, upper and lower quartiles, and extent of outliers. Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production. Time-zero is word onset as manually labeled by inspecting the spectrogram. (D) Probability distribution function of phoneme offsets color-coded from phonemes in (C). These distributions were generated from a subset of subjects ( n = 40) with sufficiently clear audio for automated phonemic transcription.

    Article Snippet: Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production.

    Techniques: Standard Deviation, Whisker Assay, Labeling, Generated